Análisis de ingredientes en recetas

Análisis de https://www.nature.com/articles/srep00196.pdf

Podemos usar read_lines_chunked si el archivo original es grande. En este ejemplo, filtramos las recetas East Asian:

library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5     ✓ purrr   0.3.4
✓ tibble  3.1.6     ✓ dplyr   1.0.7
✓ tidyr   1.1.4     ✓ stringr 1.4.0
✓ readr   2.1.1     ✓ forcats 0.5.1
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limpiar <- function(lineas, ...){
  str_split(lineas, ',') |>  
    keep(~.x[1] == 'EastAsian') |> 
    map(~.x[-1]) |>  # quitar tipo de cocina
    map(~.x[nchar(.x) > 0]) # quitar elementos vacios
}
callback_limpiar <- ListCallback$new(limpiar)
filtrado <- read_lines_chunked('../datos/recetas/srep00196-s3.csv',
                    skip = 1, callback = callback_limpiar, chunk_size = 1000)
recetas <-  filtrado |> flatten()

recetas[1:10]

[[1]]
[1] "beef_broth" "egg"        "soy_sauce"  "soybean"   

[[2]]
[1] "sesame_oil"          "beef"                "roasted_sesame_seed"
[4] "matsutake"           "black_pepper"        "scallion"           
[7] "garlic"              "soy_sauce"          

[[3]]
[1] "vinegar"             "roasted_sesame_seed" "cayenne"            
[4] "scallion"            "garlic"              "soybean"            
[7] "cucumber"            "rice"               

[[4]]
 [1] "beef"                "roasted_sesame_seed" "soy_sauce"          
 [4] "cayenne"             "ginger"              "scallion"           
 [7] "lettuce"             "garlic"              "vegetable"          
[10] "sake"               

[[5]]
[1] "garlic"    "fish"      "cayenne"   "soy_sauce" "potato"   

[[6]]
 [1] "sweet_potato"        "onion"               "roasted_sesame_seed"
 [4] "soy_sauce"           "cayenne"             "ginger"             
 [7] "soybean"             "vegetable"           "cabbage"            
[10] "rice"                "chicken"             "sesame_oil"         

[[7]]
 [1] "sesame_oil"          "radish"              "fish"               
 [4] "black_pepper"        "ginger"              "garlic"             
 [7] "seaweed"             "shrimp"              "beef"               
[10] "roasted_sesame_seed" "soy_sauce"           "cayenne"            
[13] "chinese_cabbage"     "scallion"            "sesame_seed"        
[16] "rice"               

[[8]]
 [1] "vinegar"   "radish"    "fish"      "cayenne"   "scallion" 
 [6] "cucumber"  "soybean"   "vegetable" "garlic"    "rice"     
[11] "soy_sauce"

[[9]]
[1] "radish"        "fish"          "cayenne"       "ginger"       
[5] "scallion"      "garlic"        "vegetable_oil" "soy_sauce"    

[[10]]
[1] "nut"         "cucumber"    "sesame_seed" "soybean"    

length(recetas)

[1] 2512

library(arules)

Loading required package: Matrix

Attaching package: ‘Matrix’

The following objects are masked from ‘package:tidyr’:

    expand, pack, unpack


Attaching package: ‘arules’

The following object is masked from ‘package:dplyr’:

    recode

The following objects are masked from ‘package:base’:

    abbreviate, write

length(recetas)

[1] 2512

## No hacer mucho más chico que este soporte, pues tenemos relativamente
## pocas transacciones:
pars <- list(support = 0.05,  target = 'frequent itemsets',
             ext = TRUE)
ap_recetas <- apriori(recetas, parameter = pars)

Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 125 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[242 item(s), 2512 transaction(s)] done [0.00s].
sorting and recoding items ... [41 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6 done [0.01s].
sorting transactions ... done [0.00s].
writing ... [628 set(s)] done [0.00s].
creating S4 object  ... done [0.00s].

length(ap_recetas)

[1] 628

Vemos los items frecuentes

ap_1 <- subset(ap_recetas, size(ap_recetas) == 1)
frecs <- ap_1 |> sort(by = 'support') |> DATAFRAME()
DT::datatable(frecs |> mutate_if(is.numeric, function(x) round(x, 3)))

Registered S3 methods overwritten by 'htmltools':
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Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

Y ahora examinamos combinaciones frecuentes de distintos tamaños

ap_2 <- subset(ap_recetas, size(ap_recetas) == 2)
ap_2 |> 
  subset(support > 0.20) |>
  sort(by = 'support') |>
  inspect()

Incluso hay algunas combinaciones de 4 ingredientes que ocurren con frecuencia alta: estos ingredientes son bases de salsas, combinaciones de condimentos:

ap_4 <- subset(ap_recetas, size(ap_recetas) == 4)
ap_4 |> 
  subset(support > 0.10) |>
  sort(by = 'support') |>
  inspect()

     items                    support count
[1]  {garlic,                              
      scallion,                            
      sesame_oil,                          
      soy_sauce}            0.1544586   388
[2]  {cayenne,                             
      garlic,                              
      scallion,                            
      soy_sauce}            0.1425159   358
[3]  {cayenne,                             
      garlic,                              
      ginger,                              
      scallion}             0.1337580   336
[4]  {cayenne,                             
      garlic,                              
      scallion,                            
      sesame_oil}           0.1297771   326
[5]  {black_pepper,                        
      garlic,                              
      scallion,                            
      soy_sauce}            0.1234076   310
[6]  {garlic,                              
      ginger,                              
      scallion,                            
      soy_sauce}            0.1134554   285
[7]  {cayenne,                             
      garlic,                              
      sesame_oil,                          
      soy_sauce}            0.1078822   271
[8]  {garlic,                              
      roasted_sesame_seed,                 
      scallion,                            
      sesame_oil}           0.1070860   269
[9]  {cayenne,                             
      garlic,                              
      scallion,                            
      soybean}              0.1027070   258
[10] {black_pepper,                        
      garlic,                              
      sesame_oil,                          
      soy_sauce}            0.1019108   256
[11] {cayenne,                             
      garlic,                              
      ginger,                              
      soy_sauce}            0.1015127   255
[12] {black_pepper,                        
      cayenne,                             
      garlic,                              
      scallion}             0.1007166   253

Extracción de reglas

pars <- list(support = 0.01, confidence = 0.10,
             target = 'rules',
             ext = TRUE)
reglas_recetas <- apriori(recetas, parameter = pars)

Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 25 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[242 item(s), 2512 transaction(s)] done [0.00s].
sorting and recoding items ... [88 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6 7 8 done [0.02s].
writing ... [50181 rule(s)] done [0.01s].
creating S4 object  ... done [0.01s].

agregar_hyperlift <- function(reglas, trans){
  quality(reglas) <- cbind(quality(reglas), 
    hyper_lift = interestMeasure(reglas, measure = "hyperLift", 
    transactions = trans))
  reglas
}
reglas_recetas <- agregar_hyperlift(reglas_recetas, recetas)

Análisis de pares comunes

library(arulesViz)
reglas_1 <- subset(reglas_recetas, hyper_lift > 1.1 & support > 0.1 & confidence > 0.40)
length(reglas_1)

[1] 213

reglas_tam_2 <- subset(reglas_1, size(reglas_1)==2)
#inspect(reglas_tam_2 |> sort(by = 'hyper_lift')) 
plot(reglas_1 |> subset(support > 0.2), engine = "plotly")

To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
Registered S3 method overwritten by 'data.table':
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library(tidygraph)

Attaching package: ‘tidygraph’

The following object is masked from ‘package:stats’:

    filter

library(ggraph)
df_reglas <- reglas_tam_2 |> DATAFRAME() |> rename(from=LHS, to=RHS) |> data.frame()
df_reglas$weight <- log(df_reglas$lift)
graph_1 <- as_tbl_graph(df_reglas) |>
  mutate(centrality = centrality_degree(mode = "all")) 
set.seed(881)
ggraph(graph_1, layout = 'fr') +
  geom_edge_link(aes(alpha=lift), 
                 colour = 'red',
                 arrow = arrow(length = unit(4, 'mm'))) + 
  geom_node_point(aes(size = centrality, colour = centrality)) + 
  geom_node_text(aes(label = name), size=4,
                 colour = 'gray20', repel=TRUE) +
  theme_graph(base_family = "sans")

Análisis de reglas con hyperlift alto

reglas_1 <- subset(reglas_recetas, hyper_lift > 1.5 & confidence > 0.1)
length(reglas_1)

[1] 11068

reglas_tam_2 <- subset(reglas_1, size(reglas_1) == 2)
length(reglas_tam_2)

[1] 132

library(tidygraph)
library(ggraph)
df_reglas <- reglas_tam_2 |> DATAFRAME() |> 
  rename(from=LHS, to=RHS) |> as_data_frame()

Warning: `as_data_frame()` was deprecated in tibble 2.0.0.
Please use `as_tibble()` instead.
The signature and semantics have changed, see `?as_tibble`.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

df_reglas$weight <- log(df_reglas$hyper_lift)
graph_1 <- as_tbl_graph(df_reglas) |>
  mutate(centrality = centrality_degree(mode = "all")) 
ggraph(graph_1, layout = 'fr', start.temp=100) +
  geom_edge_link(aes(alpha=lift), 
                 colour = 'red',
                 arrow = arrow(length = unit(4, 'mm'))) + 
  geom_node_point(aes(size = centrality, colour = centrality)) + 
  geom_node_text(aes(label = name), size=4,
                 colour = 'gray20', repel=TRUE) +
  theme_graph(base_family = "sans")

Exportamos para examinar en Gephi:

write_csv(df_reglas |> rename(source=from, target=to) |>
            select(-count), 'reglas.csv')

Nota

La combinación corn y starch puede deberse en parte a una separación incorrecta en el procesamiento de los datos (corn starch o maizena convertido en dos ingredientes, corn y starch):

df_reglas |> filter(from == "{corn}", to == "{starch}")

La confianza es considerablemente alta, aunque tenemos pocos datos de esta combinación. Podemos examinar algunos ejemplos:

recetas |> keep(~ "starch" %in% .x & "corn" %in% .x) |> head(10)

[[1]]
 [1] "pepper"        "ham"           "mushroom"      "starch"       
 [5] "corn"          "ginger"        "white_wine"    "nut"          
 [9] "soy_sauce"     "chicken"       "rice"          "chicken_broth"
[13] "wine"         

[[2]]
[1] "starch"        "corn"          "chicken_broth" "ginger"       
[5] "garlic"        "vegetable_oil" "soy_sauce"     "oyster"       

[[3]]
 [1] "pork"          "sesame_oil"    "starch"        "peanut"       
 [5] "soy_sauce"     "black_pepper"  "ginger"        "scallion"     
 [9] "bean"          "garlic"        "rice"          "corn"         
[13] "chicken_broth"

[[4]]
 [1] "pepper"        "celery_oil"    "starch"        "corn"         
 [5] "ginger"        "garlic"        "soybean"       "tomato"       
 [9] "vinegar"       "beef"          "soy_sauce"     "cayenne"      
[13] "scallion"      "bell_pepper"   "vegetable_oil" "rice"         
[17] "wine"         

[[5]]
 [1] "sesame_oil"    "mushroom"      "starch"        "shallot"      
 [5] "corn"          "ginger"        "white_wine"    "bean"         
 [9] "carrot"        "garlic"        "soybean"       "oyster"       
[13] "cilantro"      "onion"         "asparagus"     "chicken_broth"
[17] "celery"        "fish"          "soy_sauce"     "root"         
[21] "shiitake"     

[[6]]
 [1] "pork"          "onion"         "starch"        "corn"         
 [5] "bacon"         "soy_sauce"     "tangerine"     "ginger"       
 [9] "scallion"      "garlic"        "orange"        "vegetable_oil"
[13] "rice"          "star_anise"    "wine"         

[[7]]
 [1] "pork"          "starch"        "corn"          "chicken_broth"
 [5] "shrimp"        "celery"        "bean"          "carrot"       
 [9] "vegetable"     "cabbage"       "bread"         "egg"          
[13] "soy_sauce"     "wine"         

[[8]]
 [1] "pork"              "green_bell_pepper" "celery_oil"       
 [4] "starch"            "corn"              "garlic"           
 [7] "tomato"            "vinegar"           "onion"            
[10] "soy_sauce"         "cider"             "scallion"         
[13] "celery"            "pineapple"         "vegetable_oil"    
[16] "egg"              

[[9]]
[1] "starch"        "corn"          "bacon"         "soy_sauce"    
[5] "sherry"        "chicken"       "egg"           "chicken_broth"
[9] "cream"        

[[10]]
[1] "corn"          "starch"        "egg"           "chicken_broth"
[5] "cream"